Feedbacks Between Estuarine Metabolism and Anthropogenic CO<sub>2</sub> Accelerate Local Rates of Ocean Acidification and Hasten Threshold Exceedances
Bibliographic record
Abstract
Abstract Attribution of the ocean acidification (OA) signal in estuarine carbonate system observations is necessary for quantifying the impacts of global anthropogenic CO2 emissions on water quality, and informing managers of the efficacy of potential mitigation options. We present an analysis of observational data to characterize dynamics and drivers of seasonal carbonate system variability in two seagrass habitats of Puget Sound, WA, USA, and estimate how carbon accumulations due to anthropogenic CO2 emissions (Canth) interact with these drivers of carbonate chemistry to determine seasonally resolved rates of acidification in these habitats. Three independent simulations of Canth accumulation from 1765 to 2100 were run using two previously published methods and one novel method for Canth estimation. Our results revealed persistent seasonal differences in the magnitude of carbonate system responses to anthropogenic CO2 emissions caused by seasonal metabolic changes to the buffering capacity of estuarine waters. The seasonal variability of pHT and pCO2 is increased (while that of Ωaragonite is decreased) and acidification rates are accelerated when compared with open‐ocean estimates, highlighting how feedbacks between local metabolism and Canth can control the susceptibility of estuarine habitats to OA impacts. The changes in seasonal variability can shorten the timeline to exceedance of established physiological thresholds for endemic organisms and existing Washington State water quality criteria for pH. We highlight how Canth estimation uncertainties manifest in shallow coastal waters and limit our ability to predict impacts to coastal organisms and ecosystems from anthropogenic CO2 emissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".